Water supply telemetry data processing in apartment buildings

Abstract

The digital transformation of construction and housing and utilities sector involves data-based management. The most accessible data is telemetry of electricity, gas, heat and water consumption. Despite the fact that not all apartment buildings are equipped with meters yet, it is necessary to think about methods for processing measurement data. Intelligent data processing methods are gaining wide popularity. The purpose of this study is the processing and analysis of water consumption telemetry data. The objective of the study is to determine the periods of nighttime water consumption using the clustering method. The data of apartment meters of hourly consumption of hot and cold water are investigated. The measurement period is 1 month. Intelligent cluster analysis was conducted based on the DBSCAN machine learning model (Density-based spatial clustering of applications with noise). Clustering objects are the hours of the day. As a result of the study, the hours of night consumption of cold and hot water were allocated, both in the whole month and separately on weekdays and Sundays. The conclusion is made about the benefits of using intelligent cluster analysis of water consumption telemetry data for effective management of water resources and equipment.